Non-local wavelet coefficient contraction-based image denoising method

Abstract

The invention discloses a non-local wavelet coefficient contraction-based image denoising method, which mainly solves the problem that image details are lost when denoising is performed by adopting a traditional image denoising method. The non-local wavelet coefficient contraction-based image denoising method comprises the following implementation steps: (1) establishing noise image-contained similarity groups, performing two-dimension wavelet transformation on similarity blocks in the similarity groups, and calculating non-local means of wavelet coefficients of the similarity groups; (2) contracting the wavelet coefficients by using a double-L1 norm model, then, performing wavelet inverse transformation to obtain estimated values of the similarity blocks, and performing integration on the estimated values to obtain primary estimation images; (3) performing residual cover on the primary estimation image, and executing the step (1) and the step (2) to obtain base estimation images; (4) establishing similarity groups of the base estimation images, and further obtaining the noise image-contained similarity groups; (5) performing Wiener collaborative filtering on the noise image-contained similarity groups to obtain denoised images. According to the non-local wavelet coefficient contraction-based image denoising method disclosed by the invention, while noise is smoothened, edge textures of images can be kept better. The non-local wavelet coefficient contraction-based image denoising method can be used for denoising processing of natural images.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image denoising method based on non-local wavelet coefficient shrinkage, which can be used for denoising processing of natural images. Background technique [0002] Images are an important source of information for people, but images are often disturbed by various noises during the generation and transmission process, which not only affects the visual effect of the image, but also hinders the follow-up work such as feature extraction and target recognition. Therefore, image denoising is a crucial part of the image processing field. [0003] The purpose of image denoising is to restore a high-quality and clear image from a noisy image, while denoising while maintaining the inherent feature information of the image as much as possible. At present, a large number of denoising methods have been proposed, among which the regularization method has been widely studied, whic...

AI Technical Summary

Problems solved by technology

However, due to the one-dimensional transformation between blocks in this method, some details of the image are weakened, resulting in blurred edge areas.

The CSR method combines dictionary learning and structural clustering to make the sparse coding noise of the image small enough to improve the denoising effect, but this method is time-consuming to implement, and the denoising effect on some edges is still not ideal

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